Data processing device, data processing method, and program

ABSTRACT

A data processing device includes: a data obtaining section obtaining time series data on a total value of current consumed by a plurality of electric apparatuses; and a parameter estimating section obtaining a model parameter when states of operation of the plurality of electric apparatuses are modeled by a factorial HMM on a basis of the obtained time series data.

CROSS REFERENCES TO RELATED APPLICATIONS

The present application claims priority to Japanese Priority PatentApplication JP 2010-208231 filed in the Japan Patent Office on Sep. 16,2010, the entire content of which is hereby incorporated by reference.

BACKGROUND

The present application relates to a data processing device, a dataprocessing method, and a program, and particularly to a data processingdevice, a data processing method, and a program that make it possible toestablish a method which facilitates the calculation and adjustment of aparameter and which eliminates a need for an advance database in a caseof estimating an electric apparatus from current information obtained.

Techniques of estimating an electric apparatus connected beyond adistribution board from information on current measured by thedistribution board are referred to as non-intrusive load monitoring(hereinafter referred to as NILM), and have been studied since the1980s. NILM has great advantages of not requiring a measuring instrumentfor each of individual electric apparatuses (loads) and being able tograsp the states of all the electric apparatuses connected beyond onepoint on the basis of only a result of measurement at the one point.

As a representative technique of NILM, U.S. Pat. No. 4,858,141(hereinafter referred to as Patent Document 1), for example, discloses atechnique of identifying an electric apparatus by calculating real powerand reactive power from measurements of current and voltage andclustering amounts of change in the real power and the reactive power.The amounts of change are obtained because the real power and thereactive power being measured change when the electric apparatus isturned on and off.

FIG. 1 is a diagram shown as FIG. 8 in Patent Document 1. In FIG. 1,real power and reactive power when a refrigerator and a heater are onand off are plotted on a two-dimensional plane having the real power andthe reactive power as axes thereof. FIG. 1 shows that the on and offstates of the electric apparatuses are plotted at positions symmetricwith respect to a point.

The method of Patent Document 1 obtains difference at the on and offtimes, and thus uses only information on the moments of changes. Inaddition, a change point detector (change detector) is necessary, andwhen the change point detector fails (misses detecting turn-on orturn-off, or excessively makes erroneous detection of changes), anentire process in a subsequent stage fails.

That is, the method of Patent Document 1 has the following problems.First, because difference at on and off times is obtained, onlyinformation on the moments of changes is used. Second, it is difficultto adjust a threshold value for change point detection, and when achange point detector (net change detection) fails, an entire process ina subsequent stage fails. Third, while the method of Patent Document 1was able to be applied because many household electric appliances in the1980s were simple loads, states of many modern electric apparatusescannot be classified into an on state and an off state alone, so thatthe method of Patent Document 1 does not function well.

In order to deal with recent electric apparatuses that consume power ina complex manner, there has arisen a need to perform some complexprocess also on the side of NILM. As attempts to meet the need, manymethods using a discriminative model (discriminant model,classification) have been proposed. There are for example methodsdisclosed in Japanese Patent Laid-Open No. 2001-330630 and PCT PatentPublication No. WO01/077696 (hereinafter referred to as Patent Documents2 and 3) as methods using an LMC (Large Margin Classifier) such as asupport vector machine or the like for a discriminative model.

A discriminative model such as Adaboost, a support vector machine, orthe like is known to exhibit very high discriminating performance when afeature quantity is selected well and there is a sufficiently largeamount of sample data for learning. This method may therefore beconsidered to be effective in improving accuracy. On the other hand,however, in the case of a discriminative model, unlike a generativemodel such as an HMM or the like, it is necessary to prepare learningdata in advance and complete learning, and it is further necessary toretain results of the learning as a database. That is, there is adisadvantage in that unknown electric apparatuses cannot be handled.

There are techniques for making discrimination by a simple linear modelas in Japanese Patent Laid-Open No. 2008-039492 (hereinafter referred toas Patent Document 4) and Shinkichi Inagaki, Tsukasa Egami, TatsuyaSuzuki, Hisahide Nakamura, and Koichi Ito, “Non-Intrusive Type OperationState Monitoring System for Electric Apparatuses—Solution Based onInteger Programming with Attention Directed to Discrete States ofOperation—,” Proceedings of 42th Workshop on Discrete Event Systems, TheSociety of Instrument and Control Engineers, pp. 33-38, 2007(hereinafter referred to as Non-Patent Document 1). However, thetechniques have the same problems as the techniques disclosed in PatentDocuments 2 and 3 in that an advance database is necessary. Othermethods using a database prepared in advance include methods proposed asJapanese Patent Laid-Open Nos. 2006-017456 and 2009-257952 (hereinafterreferred to as Patent Documents 5 and 6).

After all, the above-described methods in the past involve a tradeoffbetween accuracy and a method not requiring advance registration.Household electric apparatuses have recently been greatly diversified,and a discriminative model that needs advance learning is considered tobe effectively unfit for household use. A method not requiring advanceregistration is therefore more desirable.

Accordingly, attempts to use a generative model rather than adiscriminative model that needs advance learning have already been made.For example, techniques in which a hidden Markov model (HMM) is appliedas a generative model have been proposed (see Bons M., Deville Y.,Schang D. 1994. Non-intrusive electrical load monitoring using HiddenMarkov Models. Third International Energy Efficiency and DSM Conference,October 31, Vancouver, Canada., p. 7 (Non-Patent Document 2) andHisahide Nakamura, Koichi Ito, and Tatsuya Suzuki, “Electric ApparatusOperation Condition Monitoring System Based on Hidden Markov Model,”IEEJ Transactions on Power and Energy, Vol. 126, No. 12, pp. 1223-1229,2006 (Non-Patent Document 3), for example).

However, in a case where a simple HMM is applied as a generative model,the number of states explodes (becomes enormous) as the number ofelectric apparatuses is increased, and a practical system cannot beconstructed. For example, supposing that each electric apparatus has twoon and off states, and that the number of electric apparatuses is n, anecessary number of states is 2n. Further, the size of state transitionprobability is the square of 2n (2n)2. Supposing that there are 20electric apparatuses in total in an ordinary household (which are by nomeans a large number in recent years), a necessary number of states is220=1,048,576, and the size of state transition probability is1,099,511,627,776. This size is on the order of one terabit, and isdifficult to be handled even by personal computers in recent years.

Incidentally, the method of Patent Document 1 is also based onclustering, and can be considered to be a primitive generative model, sothat advance registration is not necessary. A method for solving aproblem without modeling in an era before an approach based on astochastic model became common, such as the method of Patent Document 1,is referred to as a heuristic method. A heuristic method may be usefulas a first step, but has problems in that parameters such as a thresholdvalue and the like increase rapidly as the method is extended, and inthat it becomes difficult to adjust the parameters.

An automatic recognition technology using computers has recently becomeable to solve various difficult problems as a result of the introductionof a stochastic model. When modeling can be performed well by astochastic model, most parameters can be obtained by maximum likelihoodestimation (ML estimation, Maximum Likelihood), posterior probabilitymaximization (MAP estimation, Maximum A Posteriori), a minimumclassification error (MCE), and the like. The use of a discriminativemodel such as a support vector machine or the like and a generativemodel such as an HMM or the like corresponds to modeling by a stochasticmodel.

SUMMARY

From the above, an NILM method solving the following three points isdesired. First, there is a desire to solve the problem of difficulty inparameter adjustment such as the problem of a heuristic method, that is,the ease of parameter adjustment is desired. Second, there is a desireto eliminate a need for an advance database because it is difficult tohandle new electric apparatuses that have recently increased in numberwhen an advance database is necessary as in the case of a discriminativemodel. Third, because a parameter estimating algorithm in which thenumber of states explodes cannot practically provide a solution, thenumber of states needs to be a practical number that makes parametercalculation possible.

The present application has been made in view of such a situation. It isdesirable to establish a method which facilitates the calculation andadjustment of a parameter and which eliminates a need for an advancedatabase in a case of estimating an electric apparatus from currentinformation obtained.

According to an embodiment, there is provided a data processing deviceincluding: a data obtaining section obtaining time series data on atotal value of current consumed by a plurality of electric apparatuses;and a parameter estimating section obtaining a model parameter whenstates of operation of the plurality of electric apparatuses are modeledby a factorial HMM on a basis of the obtained time series data.

According to an embodiment, there is provided a data processing methodof a data processing device, the data processing device including a dataobtaining section obtaining time series data and a parameter estimatingsection obtaining a model parameter when modeling is performed by afactorial HMM on a basis of the obtained time series data, the dataprocessing method including: obtaining time series data on a total valueof current consumed by a plurality of electric apparatuses; andobtaining a model parameter when states of operation of the plurality ofelectric apparatuses are modeled by a factorial HMM on a basis of theobtained time series data.

According to an embodiment, there is provided a program for making acomputer function as: a data obtaining section obtaining time seriesdata on a total value of current consumed by a plurality of electricapparatuses; and a parameter estimating section obtaining a modelparameter when states of operation of the plurality of electricapparatuses are modeled by a factorial HMM on a basis of the obtainedtime series data.

In an embodiment, time series data on a total value of current consumedby a plurality of electric apparatuses is obtained, and a modelparameter when states of operation of the plurality of electricapparatuses are modeled by a factorial HMM is obtained on a basis of theobtained time series data.

According to an embodiment, it is possible to establish a method whichfacilitates the calculation and adjustment of a parameter and whicheliminates a need for an advance database in a case of estimating anelectric apparatus from current information obtained.

Additional features and advantages are described herein, and will beapparent from the following Detailed Description and the figures.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a diagram of assistance in explaining a method of PatentDocument 1 in the past;

FIG. 2 is a diagram of assistance in explaining an outline of anelectric apparatus estimating device as a data processing device towhich the present technology is applied;

FIGS. 3A and 3B are diagrams of assistance in explaining differencebetween a normal HMM and a factorial HMM;

FIG. 4 is a diagram showing correspondence between the factorial HMM andeach electric apparatus in FIG. 2;

FIG. 5 is a block diagram showing an example of configuration of theelectric apparatus estimating device in FIG. 2;

FIG. 6 is a block diagram showing an example of detailed configurationof a sensor section;

FIG. 7 is a diagram showing an example of a current measuring sectionand a voltage measuring section;

FIG. 8 is a diagram showing an example of time series data of currentvalues measured by the current measuring section;

FIG. 9 is a diagram showing correspondences between a model parameter ofthe factorial HMM and events in NILM;

FIG. 10 is a flowchart of assistance in explaining a parameterestimating process by a completely factorized variational method;

FIG. 11 is a flowchart of assistance in explaining details of an E stepprocess in FIG. 10;

FIG. 12 is a flowchart of assistance in explaining details of an M stepprocess in FIG. 10;

FIG. 13 is a flowchart of assistance in explaining a parameterestimating process by a structured variational method;

FIG. 14 is a flowchart of assistance in explaining details of an E stepprocess in FIG. 13;

FIG. 15 is a flowchart of assistance in explaining an M step process bya parameter estimating section;

FIG. 16 is a diagram showing results of parameter estimation for anelectric apparatus whose original number K of states is three;

FIG. 17 is a flowchart of assistance in explaining an identicalapparatus determining process by an identical apparatus determiningsection;

FIG. 18 is a flowchart of assistance in explaining a process of thewhole of the electric apparatus estimating device in FIG. 5; and

FIG. 19 is a block diagram showing an example of configuration of anembodiment of a computer to which the present technology is applied.

DETAILED DESCRIPTION

Embodiments of the present application will be described below in detailwith reference to the drawings.

An outline of an electric apparatus estimating device as a dataprocessing device to which the present technology is applied will firstbe described with reference to FIG. 2.

In a house or the like, electricity supplied from an electric powercompany is first drawn into a distribution board 11, and then suppliedfrom the distribution board 11 to electric apparatus 12 installed inplaces within the house. For example, in the example of FIG. 2,electricity is supplied from the distribution board 11 to a lightingdevice (light bulb) 12-1, an air conditioner 12-2, a washing machine12-3, a refrigerator 12-4, and a television receiver 12-5 as theelectric apparatus 12.

The electric apparatus estimating device 1 to which the presenttechnology is applied estimates the states of operation of the pluralityof electric apparatuses 12 by measuring a total value of currentconsumed in a combination of states of use of the plurality of electricapparatuses 12 installed in the respective places within the house on asecondary side of a main breaker of the distribution board 11. On thebasis of a result of the estimation, the electric apparatus estimatingdevice 1 displays the present state of operation of each piece ofelectric apparatus 12, and predicts the states of operation of theelectric apparatus 12 in the future after passage of a predeterminedtime from a present time.

Factorial HMM

The electric apparatus estimating device 1 estimates the state ofoperation of each piece of electric apparatus 12 using a factorial HMM(Hidden Markov Model) as analyzing means of NILM. In other words, theelectric apparatus estimating device 1 obtains a model parameter whenthe state of operation of each piece of electric apparatus 12 is modeledby the factorial HMM.

Accordingly, the factorial HMM will first be described in brief. FIGS.3A and 3B are diagrams representing a normal HMM and a factorial HMM bygraphical models.

FIG. 3A is a graphical model representing a normal HMM. FIG. 3B is agraphical model representing a factorial HMM.

In the normal HMM, one state variable S_(t) corresponds to observationdata Y_(t) at time t. The factorial HMM is different from the normal HMMin that there are a plurality of state variables S_(t), that is, S_(t)⁽¹⁾, S_(t) ⁽²⁾, S_(t) ⁽³⁾, . . . , S_(t) ^((m)), . . . , S_(t) ^((M)) (Mstate variables in FIG. 3B), and in that one piece of observation dataY_(t) is generated from the plurality of state variables S_(t) ⁽¹⁾ toS_(t) ^((M)).

FIG. 4 is a diagram showing the factorial HMM in FIG. 3B in associationwith each piece of electric apparatus 12 shown in FIG. 2.

The M state variables S⁽¹⁾ to S^((M)) of the factorial HMM correspond torespective electric apparatuses 12. In addition, the state value of astate variable S^((m)) corresponds to the state of the electricapparatus 12 (for example two on and off states).

More specifically, state values S₁ ⁽¹⁾ to S_(t) ⁽¹⁾ according to thepassage of time of the first state variable S⁽¹⁾ of the M statevariables S⁽¹⁾ to S^((M)) correspond to states of a predeterminedelectric apparatus 12 (for example the refrigerator 12-4). In addition,state values S₁ ⁽²⁾ to S_(t) ⁽²⁾ according to the passage of time of thesecond state variable S⁽²⁾ correspond to states of a predeterminedelectric apparatus 12 (for example the television receiver 12-5).Similarly, state values S₁ ^((m)) to S_(t) ^((m)) according to thepassage of time of the mth state variable S^((m)) correspond to statesof a predetermined electric apparatus 12 (for example the washingmachine 12-3).

In addition, total values of current consumed in a combination of statesof use of the plurality of electric apparatuses 12 installed in therespective places within the house are obtained as observation data Y₁to Y_(t).

Incidentally, the mth state variable S^((m)) of the M state variablesS⁽¹⁾ to S^((M)) will hereinafter be described also as an mth factor or afactor m.

Details of the factorial HMM are described in Zoubin Ghahramani, andMichael I. Jordan, “Factorial Hidden Markov Models',” Machine LearningVolume 29, Issue 2-3, November/December 1997 (hereinafter referred to asDocument X).

Example of Configuration of Electric Apparatus Estimating Device 1

FIG. 5 is a block diagram showing an example of configuration of theelectric apparatus estimating device 1.

The electric apparatus estimating device 1 includes a sensor section 21,a parameter estimating section 22, a database 23, an identical apparatusdetermining section 24, a state predicting section 25, an apparatusidentifying section 26, and a display section 27.

The sensor section 21 measures (obtains) a total value of currentconsumed in a combination of states of use of the plurality of electricapparatuses 12 installed in the respective places within the house asobservation data Y_(t) (t=1, 2, . . . , T). The sensor section 21supplies the observation data to the parameter estimating section 22.

The parameter estimating section 22 obtains a model parameter when thestate of operation of each piece of electric apparatus 12 is modeled bythe factorial HMM on the basis of the observation data {Y₁, Y₂, Y₃, . .. , Y_(t), . . . , Y_(T)} as time series data on the total value ofcurrent consumed by the plurality of electric apparatuses 12. The modelparameter obtained by the learning process of the factorial HMM isstored in the database 23.

In addition, when new observation data Y_(t) is supplied from the sensorsection 21, the parameter estimating section 22 obtains the modelparameter from the database 23, and updates the model parameter. Thatis, the present model parameter is updated on the basis of the newobservation data Y_(t).

The identical apparatus determining section 24 detects a plurality offactors modeling an identical electric apparatus 12 among the M factors,and stores a result of the detection in the database 23. In other words,the identical apparatus determining section 24 determines whether afirst factor m₁ and a second factor m₂ (m₁≠m₂) among the M factorsrepresent an identical electric apparatus 12, and registers a result ofthe determination in the database 23.

The factorial HMM itself is a general-purpose method for modeling timeseries data, and is applicable to various problems other than NILM.Thus, an estimating method using a factorial HMM in the past cannot beapplied well to NILM as it is. One of problems of the estimating methodusing a factorial HMM in the past as it is a case where one electricapparatus 12 may be modeled by a plurality of factors. Accordingly, whenone electric apparatus 12 is represented by a plurality of factors, theidentical apparatus determining section 24 detects that the plurality offactors correspond to the identical electric apparatus 12.

The state predicting section 25 predicts the state of the factor m(electric apparatus 12 of the factor m) in the future after passage of apredetermined time from a present time using the model parameter storedin the database 23. The factorial HMM is of course a probabilistic modelbased on an HMM, and is thus able to predict state probability at afuture time probabilistically. The state predicting section 25 outputs aresult of the prediction of the state of the factor m in the futureafter the passage of the predetermined time from the present time to arelated apparatus controlling section or the like not shown in FIG. 5,for example. The related apparatus controlling section can therebycontrol another electric apparatus 12 related to the electric apparatus12 of the factor m on the basis of the result of the prediction of thestate of the factor m (electric apparatus 12 of the factor m) in thefuture.

The apparatus identifying section 26 identifies the electric apparatus12 to which the factor m corresponds. That is, in a process up to thestate predicting section 25, state changes of each factor m can berecognized and predicted, but the electric apparatus 12 to which eachfactor m corresponds, the electric apparatus 12 being installed in thecorresponding place within the house, cannot be identified. Theapparatus identifying section 26 associates each factor m with anelectric apparatus 12 within the house. For example, the apparatusidentifying section 26 stores a current waveform pattern typical of eachproduct type (the washing machine, the refrigerator, the airconditioner, and the like) of electric apparatus 12, compares thetypical current waveform pattern with actually obtained observation dataY of factors, and identifies the electric apparatus 12 (type of theelectric apparatus 12). In addition, for example, the apparatusidentifying section 26 receives an input of a type of electric apparatus12 which input is provided for each factor by a user on an operatingsection not shown in FIG. 5, and associates the input type (product) ofthe electric apparatus 12 with the observation data Y of the factors.

The display section 27 is formed by a liquid crystal display or thelike. The display section 27 displays the state of operation of theelectric apparatus 12 identified by the apparatus identifying section 26on the basis of a result of estimation by the parameter estimatingsection 22.

Details of each of the parts forming the electric apparatus estimatingdevice 1 will be described in the following.

Example of Detailed Configuration of Sensor Section 21

FIG. 6 is a block diagram showing an example of detailed configurationof the sensor section 21.

The sensor section 21 includes a current measuring, section 41 and avoltage measuring section 42 for measuring total values of current andvoltage of the plurality of electric apparatuses 12 installed in therespective places within the house, a current waveform cutout section43, and a time series retaining section 44.

FIG. 7 shows an example of the current measuring section 41 and thevoltage measuring section 42 in a case where electricity is supplied tothe plurality of electric apparatuses 12 by wiring of a single-phasethree-wire system.

For example, the current measuring section 41 is formed by two clamp-onammeters 54 and 55, which are clamped to two voltage lines (cables) 51and 53 connected to the secondary side of the main ampere breaker withinthe distribution board 11, and measures a current flowing through thevoltage lines.

The voltage measuring section 42 is formed by a voltmeter 56, andmeasures a voltage between predetermined lines. The voltage is measuredto synchronize the phase of the current with the phase of the voltage.It therefore suffices for the voltage measuring section 42 to measureone of a voltage between the voltage line 51 and a neutral line 52 and avoltage between the neutral line 52 and the voltage line 53. In theexample of FIG. 7, the voltmeter 56 measures the voltage between thevoltage line 51 and the neutral line 52.

Returning to FIG. 6, the current waveform cutout section 43 generates arepresentative sample by discretely reducing the time series data(current waveform) of current values obtained momently in a temporaldirection, and supplies the representative sample as observation dataY_(t) at time t to the time series retaining section 44.

FIG. 8 shows an example of the time series data of current valuesmeasured by the current measuring section 41.

A waveform in the top of FIG. 8 is the time series data of currentvalues measured by the current measuring section 41 within apredetermined period. The magnitude of amplitude of the waveformrepresents the magnitude of consumed current.

A waveform in the middle of FIG. 8 represents time series data of fourcycles which time series data is included in the time series data shownin the top of FIG. 8 in an enlarged manner. Further, a waveform in thebottom of FIG. 8 represents time series data of one cycle which timeseries data is included in the time series data of four cycles in themiddle of FIG. 8 in an enlarged manner.

The current waveform cutout section 43 first synchronizes the phase ofthe current waveform with the phase of the voltage. Specifically, thecurrent waveform cutout section 43 treats a moment of zero crossing ofthe voltage value as a phase zero of the current waveform. The currentwaveform cutout section 43 next samples the current waveform for onecycle at predetermined time intervals. This is because sampling allcycles would result in a massive amount of data. When the currentwaveform of one cycle is obtained as one sample in the period of onesecond, for example, an amount of data can be reduced to 1/50 in aregion of a commercial power frequency of 50 Hz.

When one time of sampling is performed at 50 Hz, that is, the currentwaveform of one cycle is sampled at 50 Hz in the region of a commercialpower frequency of 50 Hz, 1000 current values are obtained by one timeof sampling, as shown in the bottom of FIG. 8. The current waveformcutout section 43 expresses the 1000 current values obtained by one timeof sampling by a 1000-dimensional current value vector, and supplies the1000-dimensional current value vector as observation data Y_(t) atpresent time t to the time series retaining section 44. The observationdata Y_(t) is thus a D-dimensional (D=1000) real vector.

Returning to FIG. 6, the time series retaining section 44 stores andretains the observation data Y_(t) sequentially supplied from thecurrent waveform cutout section 43. Thereby, observation data {Y₁, Y₂,Y₃, . . . , Y_(t), . . . , Y_(T)} one sample of which is expressed by a1000-dimensional current value vector is stored in the time seriesretaining section 44.

Incidentally, while the observation data is discretely reduced in thetemporal direction by simply performing one time of sampling in theperiod of one second in the above-described example, a method ofdiscretely reducing the observation data is not limited to this. Forexample, an average vector of current value vectors of 50 samples may becalculated, and a result of the calculation may be set as theobservation data Y_(t) of one sample. In addition, a current valuevector closest to an average vector of 50 samples among the currentvalue vectors of the 50 samples may be set as the observation data Y_(t)of one sample. Alternatively, the current value vectors of 50 samplesmay be clustered by a K-means method and an average vector of a clusterof a largest number of samples may be set as the observation data Y_(t)of one sample, or an average vector of two samples with a minimumdistance between the vectors among the current value vectors of 50samples may be set as the observation data Y_(t) of one sample.

On the basis of the observation data {Y₁, Y₂, Y₃, . . . , Y_(t), . . . ,Y_(T)} stored in the time series retaining section 44, the parameterestimating section 22 estimates the model parameter of the factorial HMMwhen the states of operation of the plurality of electric apparatuses 12are modeled.

Factorial HMM

Details of the parameter estimating section 22 will next be described.However, model parameter estimation in the factorial HMM will first bedescribed.

Supposing that hidden states for the observation data {Y₁, Y₂, Y₃, . . ., Y_(t), . . . , Y_(T)} are {S₁, S₂, S₃, . . . , S_(t), . . . , S_(T)},the joint probability of a hidden state S_(t) and observation data Y_(t)is given by the following Equation (1).

$\begin{matrix}{{P\left( \left\{ {S_{t},Y_{t}} \right\} \right)} = {{P\left( S_{1} \right)}{P\left( {Y_{1}❘S_{1}} \right)}{\prod\limits_{t = 2}^{T}{{P\left( {S_{t}❘S_{t - 1}} \right)}{P\left( {Y_{t}❘S_{t}} \right)}}}}} & (1)\end{matrix}$

P(S₁) in Equation (1) denotes initial probability, P(S_(t)|S_(t−1)) inEquation (1) denotes state transition probability, and P(Y_(t)|S_(t)) inEquation (1) denotes observation probability. P(S₁), P(S_(t)|S_(t−1)),and P(Y_(t)|S_(t)) in Equation (1) are calculated by Equation (2),Equation (3), and Equation (4), respectively.

$\begin{matrix}\begin{matrix}{{P\left( S_{1} \right)} = {\prod\limits_{m = 1}^{M}{P\left( S_{1}^{(m)} \right)}}} \\{= {\prod\limits_{m = 1}^{M}\pi^{(m)}}}\end{matrix} & (2) \\\begin{matrix}{{P\left( {S_{t}❘S_{t - 1}} \right)} = {\prod\limits_{m = 1}^{M}{P\left( {S_{t}^{(m)}❘S_{t - 1}^{(m)}} \right)}}} \\{= {\prod\limits_{m = 1}^{M}A^{(m)}}}\end{matrix} & (3) \\\begin{matrix}\begin{matrix}{{P\left( {Y_{t}❘S_{t}} \right)} = {{Normal}\left( {{Y_{t} :: \mu_{t}},C} \right)}} \\{= {\frac{1}{\sqrt{\left( {2\pi} \right)^{D}{C}}}{\exp\left( {{- \frac{1}{2}}\left( {Y_{t} - \mu_{t}} \right)^{\prime}{C^{- 1}\left( {Y_{t} - \mu_{t}} \right)}} \right)}}}\end{matrix} \\{{{where}\mspace{14mu}\mu_{t}} = {\sum\limits_{m = 1}^{M}{W^{(m)}{S_{t}^{(m)}.}}}}\end{matrix} & (4)\end{matrix}$

Although there is a case where a plurality of factors correspond to oneelectric apparatus 12, model parameter estimation in the factorial HMMwill be described supposing that one factor corresponds to one electricapparatus 12 as a simplest example. An electric apparatus 12corresponding to a factor m in the case where one factor corresponds toone electric apparatus 12 will be referred to also as an mth electricapparatus 12.

S_(t) ^((m)) in Equations (2) to (4) denotes the state (an on state, anoff state, a strong operation, and a weak operation or the like) of themth electric apparatus 12 at time t. Letting K be the number of statesof the mth electric apparatus 12, S_(t) ^((m)) is formed by aK-dimensional vertical vector (vector of K rows and one column).

The initial probability P(S₁) in Equation (2) is calculated bymultiplication of M pieces of π^((m)). π^((m)) denotes the initial stateprobability of the mth electric apparatus 12, and is a vertical vectorof K dimensions.

The state transition probability P(S_(t)|S_(t−1)) in Equation (3) iscalculated by multiplication of M pieces of A^((m)). A^((m)) for exampledenotes the state transition probability of the mth electric apparatus12 which state transition probability corresponds to a tendency tochange from an on state to an off state, and is formed by a squarematrix of K rows and K columns (K×K).

The observation probability P(Y_(t)|S_(t)) in Equation (4) is calculatedby a multivariate normal distribution with an observation average μ_(t)and a covariance matrix C. In Equation (4), a dash (′) denotestransposition, and “−1” at an upper right denotes a reciprocal. Inaddition, |C| denotes the absolute value of C.

W^((m)) in Equation (4) is a parameter of the observation probabilityP(Y_(t)|S_(t)) which parameter corresponds to the pattern of a currentwaveform consumed by the mth electric apparatus 12. Because the patternof the current waveform differs for each state of the electric apparatus12, W^((m)) is a matrix of D rows and K columns (D×K) with the number Dof dimensions of observation data as the number of rows and with thenumber K of states as the number of columns.

μ_(t) denotes an observation average at time t, and is obtained byadding together M column elements of the matrix W^((m)) which columnelements correspond to the state S_(t) ^((m)). In other words, μ_(t)corresponds to an addition of current values corresponding to the statesof all the electric apparatuses 12. Thus, when the observation averageμ_(t) is close to observation data Y_(t) at time t, the model parameteris plausible. The covariance matrix C corresponds to the intensity ofnoise superimposed on a current pattern, and is assumed to be common toall the electric apparatuses 12 at all times.

From the above, the model parameter estimation in the factorial HMM bythe parameter estimating section 22 is specifically to obtain theinitial state probability π^((m)) of the mth electric apparatus 12, thestate transition probability A^((m)) of the mth electric apparatus 12,the observation probability parameter W^((m)) of the mth electricapparatus 12, and the covariance matrix C. FIG. 9 shows correspondencesbetween the model parameter Φ={π^((m)), A^((m)), W^((m)), C} of thefactorial HMM and events in NILM.

The above-described Document X discloses four estimating methods asmethods for estimating the model parameter Φ={π^((m)), A^((m)), W^((m)),C}. Specifically, Document X discloses 1) exact inference, 2) inferenceusing Gibbs sampling, 3) completely factorized variational inference,and 4) structured variational inference.

The four estimating methods are each a method using an EM algorithm, andcan be adopted as a basis for an estimation process performed by theparameter estimating section 22. “1) exact inference” involves a largeamount of calculation but enables stable estimation with high accuracy,and is thus useful when used for a small-scale system having a smallnumber of electric apparatuses 12 or in a development stage. “4)structured variational inference” is most effective when consideredcomprehensively in relation to each of execution speed, an amount ofmemory used at a time of execution, and execution accuracy.

Description in the following will be made supposing that the parameterestimating section 22 performs “3) completely factorized variationalinference” and “4) structured variational inference” among the fourestimating methods.

Parameter Estimating Process by Completely Factorized Variational Method

FIG. 10 is a flowchart of a parameter estimating process by a completelyfactorized variational method, which is “3) completely factorizedvariational inference.”

First, in step S1, the parameter estimating section 22 performs aninitializing process for initializing working variables and the like inthe parameter estimating process. Specifically, for all times t andfactors m (t=1, . . . , T, m=1, . . . , M), the parameter estimatingsection 22 initializes a variational parameter θ_(t) ^((m)), theobservation probability parameter W^((m)) of the factor m, a covariancematrix C, and state transition probability A_(i,j) ^((m)). Thevariational parameter θ_(t) ^((m)) and the state transition probabilityA_(i,j) ^((m)) are assigned 1/K as an initial value. The observationprobability parameter W^((m)) of the factor in is assigned apredetermined random number as an initial value. C=aI (a is an arbitraryreal number and I is a unit matrix of D rows and D columns (D×D)) is setas an initial value for the covariance matrix C.

In step S2, the parameter estimating section 22 performs an E stepprocess for estimating state probability. Details of the E step processwill be described later with reference to FIG. 11.

In step S3, the parameter estimating section 22 performs an M stepprocess for estimating a transition and an observation parameter.Details of the M step process will be described later with reference toFIG. 12.

In step S4, the parameter estimating section 22 determines whether amodel parameter convergence condition is satisfied. For example, theparameter estimating section 22 determines that the model parameterconvergence condition is satisfied when the number of repetitions of theprocess of steps S2 to S4 has reached a predetermined number of timesset in advance, or when an amount of change in state likelihood due toupdate of the model parameter is within a predetermined value.

When it is determined in step S4 that the model parameter convergencecondition is not satisfied yet, the process returns to step S2 to repeatthe process of steps S2 to S4.

When it is determined in step S4 that the model parameter convergencecondition is satisfied, on the other hand, the parameter estimatingsection 22 ends the parameter estimating process.

Detailed Flowchart of E Step Process

Details of the E step process performed as step S2 in FIG. 10 will nextbe described with reference to a flowchart of FIG. 11.

In step S11 of the E step process, the parameter estimating section 22first assigns one to a variable m corresponding to a factor.

In step S12, the parameter estimating section 22 obtains a temporaryvariable Δ^((m)) by the following Equation (5).Δ^((m))=diag(W′ ^((m)) C ⁻¹ W ^((m)))  (5)

diag(•) in Equation (5) is a function for extracting diagonal elementsof a matrix in (•) as a vector.

In step S13, the parameter estimating section 22 obtains a temporaryvariable η_(t) ^((m)) and a variational parameter θ_(t) ^((m)) for alltimes t (t=1, . . . , T) by Equation (6) and Equation (7).

$\begin{matrix}{\mspace{20mu}{\eta_{t}^{(m)} = {Y_{t} - {\sum\limits_{l \neq m}^{M}{W^{(l)}\theta_{t}^{(l)}}}}}} & (6) \\{\theta_{t}^{(m)} = {{Softmax}\left( {{W^{\prime{(m)}}C^{- 1}\eta_{t}^{(m)}} - {\frac{1}{2}\Delta^{(m)}} + {\left( {\log\; A^{(m)}} \right)\theta_{t - 1}^{(m)}} + {\left( {\log\; A^{(m)}} \right)^{\prime}\theta_{t - 1}^{(m)}}} \right)}} & (7)\end{matrix}$

A summation (Σ) in Equation (6) denotes a sum when 1 is set to be 1 to Mexcept m. In addition, a Softmax function in Equation (7) is a functionfor performing a process expressed by Equation (8).

$\begin{matrix}{{{Softmax}\left( q_{i} \right)} = \frac{\exp\left( q_{i} \right)}{\sum\limits_{j}{\exp\left( q_{j} \right)}}} & (8)\end{matrix}$

In step S14, the parameter estimating section 22 determines whether thevariable m is equal to the number M of state variables, that is, whetherthe variational parameter ƒ_(t) ^((m)) has been obtained for all thefactors 1 to M. When it is determined in step S14 that the variable m isnot equal to the number M of state variables, the process proceeds tostep S15, where the parameter estimating section 22 increments thevariable m by one. The parameter estimating section 22 then returns theprocess to step S12. Thereby, the process of steps S12 to S14 isrepeated for the variable m after being updated.

When it is determined in step S14 that the variable in is equal to thenumber M of state variables, that is, that the variational parameterθ_(t) ^((m)) has been obtained for all the factors 1 to M, on the otherhand, the process proceeds to step S16, where the parameter estimatingsection 22 determines whether a condition for convergence of thevariational parameter θ_(t) ^((m)) is satisfied. It is determined instep S16 that the condition for convergence of the variational parameterθ_(t) ^((m)) is satisfied when the number of repetitions has reached apredetermined number of times set in advance, for example.

When it is determined in step S16 that the condition for convergence ofthe variational parameter θ_(t) ^((m)) is not satisfied, the processreturns to step S11. Then, steps S11 to S16 are performed again, andthereby the variational parameter θ_(t) ^((m)) is calculated (updated)again.

When it is determined in step S16 that the condition for convergence ofthe variational parameter θ_(t) ^((m)) is satisfied, on the other hand,the process proceeds to step S17, where the parameter estimating section22 assigns one to the variable in corresponding to a factor again.

Then, in step S18, the parameter estimating section 22 obtainsexpectation variables <S_(t) ^((m))>, <S′_(t) ^((m))>, and <S_(t−1)^((m))S′_(t) ^((m))> by the following Equations (9) to (11).

S _(t) ^((m))

=θ_(t) ^((m))  (9)

S′ _(t) ^((m))

=θ′_(t) ^((m))  (10)

S _(t−1) ^((m)) S′ _(t) ^((m))

=θ_(t−1) ^((m))θ′_(t) ^((m))  (11)

Further, in step S19, the parameter estimating section 22 obtains anexpectation variable <S_(t) ^((m))S′_(t) ^((n))> with a variable n setto 1 to M by the following Equation (12).

$\begin{matrix}{\left\langle {S_{t}^{(m)}S_{t}^{\prime{(n)}}} \right\rangle = \left\{ \begin{matrix}{\theta_{t}^{(m)}\theta_{t}^{\prime{(n)}}} & {{{if}\mspace{14mu} m} \neq n} \\{{diag}\left( \theta_{t}^{(m)} \right)} & {{{if}\mspace{14mu} m} = n}\end{matrix} \right.} & (12)\end{matrix}$

In step S20, the parameter estimating section 22 determines whether thevariable m is equal to the number M of state variables, that is, whetherthe expectation variables <S_(t) ^((m))>, <S′_(t) ^((m))>, <S_(t−1)^((m))S′_(t) ^((m))>, and <S_(t) ^((m))S′_(t) ^((n))> have been obtainedfor all the factors 1 to M.

When it is determined in step S20 that the variable m is not equal tothe number M of state variables, the process proceeds to step S21, wherethe parameter estimating section 22 increments the variable m by one.The parameter estimating section 22 then returns the process to stepS18. Thereby, the process of steps S18 to S20 is repeated for thevariable m after being updated.

When it is determined in step S20 that the variable m is equal to thenumber M of state variables, that is, that the expectation variables<S_(t) ^((m))>, <S′_(t) ^((m))>, <S_(t−1) ^((m))S′_(t) ^((m))>, and<S_(t) ^((m))S′_(t) ^((n))> have been obtained for all the factors 1 toM, on the other hand, the E step process is ended.

Detailed Flowchart of M Step Process

After the E step process is ended, the M step process in step S3 in FIG.10 is performed. FIG. 12 is a flowchart of assistance in explainingdetails of the M step process in step S3.

In step S31 of the M step process, the parameter estimating section 22obtains initial state probability π^((m)) for all the factors m=1 to Mby the following Equation (13).π^((m)) =

S ₁ ^((m))

  (13)

In step S32, the parameter estimating section 22 obtains statetransition probability A_(i,j) ^((m)) from a state S_(j) ^((m)) to astate S_(i) ^((m)) for all the factors m by the following Equation (14).

$\begin{matrix}{A_{i,j}^{(m)} = \frac{\sum\limits_{t = 2}^{T}\left\langle {S_{t,i}^{(m)}S_{{t - 1},j}^{(m)}} \right\rangle}{\sum\limits_{t = 2}^{T}\left\langle S_{{t - 1},j}^{(m)} \right\rangle}} & (14)\end{matrix}$

where S_(t−1,j) ^((m)) denotes that a state S_(j) ^((m)) before atransition is a state variable S_(t−1) ^((m)) at time t−1, and S_(t,i)^((m)) denotes that a state S_(i) ^((m)) after the transition is a statevariable S_(t) ^((m)) at time t.

In step S33, the parameter estimating section 22 obtains an observationprobability parameter W by the following Equation (15).

$\begin{matrix}{W = {\left( {\sum\limits_{t = 1}^{T}{Y_{t}\left\langle S_{t}^{\prime} \right\rangle}} \right) \cdot {{pinv}\left( {\sum\limits_{t = 1}^{T}\left\langle {S_{t}S_{t}^{\prime}} \right\rangle} \right)}}} & (15)\end{matrix}$

In Equation (15), the observation probability parameter W represents amatrix of D rows and MK columns (D×MK, where MK is a product of M and K)obtained by connecting M parameters W^((m)) of D rows and K columns(D×K) for all the factors in a column direction. Thus, the observationprobability parameter W^((m)) of the factor m is obtained by decomposingthe observation probability parameter W in the column direction. Inaddition, pinv(•) in Equation (15) is a function for obtaining apseudo-inverse matrix.

In step S34, the parameter estimating section 22 obtains a covariancematrix C by the following Equation (16).

$\begin{matrix}{C = {{\frac{1}{T}{\sum\limits_{t = 1}^{T}{Y_{t}Y_{t}^{\prime}}}} - {\frac{1}{T}{\sum\limits_{t = 1}^{T}{\sum\limits_{m = 1}^{M}{W^{(m)}\left\langle S_{t}^{(m)} \right\rangle Y_{t}^{\prime}}}}}}} & (16)\end{matrix}$

The model parameter Φ of the factorial HMM is obtained (updated) by theabove-described steps S31 to S34, and the M step process is ended toproceed with the process of step S4 in FIG. 10.

Parameter Estimating Process by Structured Variational Method

Description will next be made of a parameter estimating process by astructured variational method, which is “4) structured variationalinference.” FIG. 13 is a flowchart of a parameter estimating process bya structured variational method.

First, in step S41, the parameter estimating section 22 performs aninitializing process for initializing working variables and the like inthe parameter estimating process. Specifically, for all times t andfactors m (t=1, . . . , T, m=1, . . . , M), the parameter estimatingsection 22 initializes a variational parameter h_(t) ^((m)), anexpectation variable <S_(t) ^((m))>, the observation probabilityparameter W^((m)) of the factor m, a covariance matrix C, and statetransition probability A_(i,j) ^((m)). The variational parameter h_(t)^((m)) is assigned 1/K as an initial value. The parameters other thanthe variational parameter h_(t) ^((m)) are assigned similar values tothose of the initializing process of step S1 described above.

The processes of following steps S42 to S44 are similar to those ofsteps S2 to S4, respectively, in FIG. 10, and therefore descriptionthereof will be omitted.

Detailed Flowchart of E Step Process

Details of the E step process performed as step S42 in FIG. 13 will nextbe described with reference to a flowchart of FIG. 14.

In step S61 of the E step process in the parameter estimating process bythe structured variational method, the parameter estimating section 22first assigns one to a variable m corresponding to a factor.

Then, in step S62, the parameter estimating section 22 obtains atemporary variable Δ^((m)) by the above-described Equation (5). That is,the processes of steps S61 and S62 are similar to the above-describedsteps S11 and S12 in FIG. 11.

In step S63, the parameter estimating section 22 obtains a temporaryvariable η_(t) ^((m)) and a variational parameter h_(t) ^((m)) for alltimes t (t=1, . . . , T) by Equation (17) and Equation (18).

$\begin{matrix}{\eta_{t}^{(m)} = {Y_{t} - {\sum\limits_{l \neq m}^{M}{W^{(l)}\left\langle S_{t}^{(l)} \right\rangle}}}} & (17) \\{h_{t}^{(m)} = {\exp\left( {{W^{\prime{(m)}}C^{- 1}\eta_{t}^{(m)}} - {\frac{1}{2}\Delta^{(m)}}} \right)}} & (18)\end{matrix}$

In step S64, the parameter estimating section 22 determines whether thevariable m is equal to the number M of state variables, that is, whetherthe variational parameter h_(t) ^((m)) has been obtained for all thefactors 1 to M. When it is determined in step S64 that the variable m isnot equal to the number M of state variables, the process proceeds tostep S65, where the parameter estimating section 22 increments thevariable m by one. The parameter estimating section 22 then returns theprocess to step S62. Thereby, the process of steps S62 to S64 isrepeated for the variable m after being updated.

When it is determined in step S64 that the variable m is equal to thenumber M of state variables, that is, that the variational parameterh_(t) ^((m)) has been obtained for all the factors 1 to M, on the otherhand, the process proceeds to step S66, where the parameter estimatingsection 22 determines whether a condition for convergence of thevariational parameter h_(t) ^((m)) is satisfied. The parameterestimating section 22 determines in step S66 that the condition forconvergence of the variational parameter h_(t) ^((m)) is satisfied whenthe number of repetitions has reached a predetermined number of timesset in advance, for example.

When it is determined in step S66 that the condition for convergence ofthe variational parameter h_(t) ^((m)) is not satisfied, the processreturns to step S61. Then, steps S61 to S66 are performed again, andthereby the variational parameter h_(t) ^((m)) is calculated (updated)again.

When it is determined in step S66 that the condition for convergence ofthe variational parameter h_(t) ^((m)) is satisfied, on the other hand,the process proceeds to step S67, where the parameter estimating section22 obtains expectation variables <S_(t) ^((m))> and <S_(t−1)^((m))S′_(t) ^((m))> using a forward-backward algorithm using thevariational parameter h_(t) ^((m)) and the state transition probabilityA_(i,j) ^((m)).

In step S68, the parameter estimating section 22 obtains an expectationvariable <S_(t) ^((m))S′_(t) ^((n))> with a variable n set to 1 to M bythe following Equation (19). Then, the parameter estimating section 22ends the E step process, and returns to FIG. 13.

$\begin{matrix}{\left\langle {S_{t}^{(m)}S_{t}^{\prime{(n)}}} \right\rangle = \left\{ \begin{matrix}{\left\langle S_{t}^{(m)} \right\rangle\left\langle S_{t}^{\prime{(n)}} \right\rangle} & {{{if}\mspace{14mu} m} \neq n} \\{{diag}\left( \left\langle S_{t}^{(m)} \right\rangle \right)} & {{{if}\mspace{14mu} m} = n}\end{matrix} \right.} & (19)\end{matrix}$

The M step process performed after the E step process in the parameterestimating process by the structured variational method is similar tothe M step process in the parameter estimating process by the completelyfactorized variational method, which M step process has been describedwith reference to FIG. 12, and therefore description thereof will beomitted.

Method of Factorial HMM by Parameter Estimating Section 22

The parameter estimating process by the completely factorizedvariational method and the parameter estimating process by thestructured variational method among the four estimating methods forobtaining the model parameter Φ of the factorial HMM disclosed inDocument X have been described above.

The factorial HMM itself is a general-purpose time series data modelingmethod, and is applicable to various problems other than NILM. However,because the factorial HMM is a general-purpose model, the following twoproblems occur when the factorial HMM is applied to NILM as it is.

1) There is a factor with “negative power consumption,” which cannotphysically exist. This corresponds to a fact that the parameter W^((m))of the factorial HMM can assume even a negative value because a degreeof freedom of parameters of the factorial HMM is too high.

2) The factors and the electric apparatuses 12 installed in the housemay not be in one-to-one correspondence. In other words, there is a casewhere one electric apparatus 12 corresponds to a plurality of factors.This is because one piece of observation data can be explained in two ormore ways due to high power of expression of the factorial HMM.

Accordingly, the parameter estimating section 22 in the electricapparatus estimating device 1 to which the present technology is appliedadopts a method obtained by making the following improvements to thefactorial HMM disclosed in Document X serving as a basis so that thefactorial HMM is applied to NILM.

First, as an improvement in the factorial HMM, the parameter estimatingsection 22 adds a constraint such that the observation probabilityparameter W(m) in the factorial HMM is a nonnegative matrix (nonnegativeconstraint). Because alternating-current power is supplied, the currentvalue as observation data Y assumes a positive value and a negativevalue alternately. Accordingly, the parameter estimating section 22converts the current value as observation data Y into only positivevalues by performing one of the following methods as preprocessing.Specifically, 1) the parameter estimating section 22 uses the absolutevalue of the current value as observation data Y, 2) the parameterestimating section 22 uses only a part corresponding to half a cycle inwhich positive values are taken as observation data Y, or 3) theparameter estimating section 22 uses a power value (product of thecurrent value and a voltage value) as observation data Y.

Further, the parameter estimating section 22 obtains an observationprobability parameter W minimizing an objective function expressed bythe following Equation (20) by constrained quadratic programming withoutusing the above-described Equation (15).

$\begin{matrix}{{\min{{{F \cdot W^{vertical}} - g}}^{2}\mspace{14mu}{where}}{{W^{vertical} \geqq 0},{F = \left\lbrack {\quad\underset{\underset{D}{︸}}{\begin{matrix}{\sum\limits_{t = 1}^{T}\left\langle {S_{t}S_{t}^{\prime}} \right\rangle} & \; & \; & 0 \\\; & {\sum\limits_{t = 1}^{T}\left\langle {S_{t}S_{t}^{\prime}} \right\rangle} & \; & \; \\\; & \; & \ddots & \; \\0 & \; & \; & {\sum\limits_{t = 1}^{T}\left\langle {S_{t}S_{t}^{\prime}} \right\rangle}\end{matrix}}\quad} \right\rbrack},{g = {{reshape\_ to}{\_ vertical}{\_ vector}\left( {\sum\limits_{t = 1}^{T}{Y_{t}\left\langle S_{t}^{\prime} \right\rangle}} \right)}}}} & (20)\end{matrix}$

Specifically, in place of the above-described Equation (15), theparameter estimating section 22 obtains W^(vertical) that minimizes thesquare of the absolute value of a difference between a product vector ofF and W^(vertical) and a g-vector as an objective function under aconstraint condition W^(vertical)≧0. Equation (20) means the obtainmentof W^(vertical) that minimizes an error between the pattern of a currentwaveform expressed by the parameter W^((m)) and the pattern of a totalvalue of the above-described consumed current expressed by theobservation data Y for all the factors of the factorial HMM.Incidentally, reshape_to_vertical_vector(•) is a function representingan operation of transforming for example a matrix of a rows and bcolumns (a×b) into a vector of ab rows and one column (ab×1, where ab isa product of a and b) by connecting the column elements of the matrix toeach other vertically.

The parameter W^(vertical) to be obtained has a relationW ^(vertical)=reshape_to_vertical_vector(W)

Thus, the observation probability parameter W is obtained by convertingW^(vertical) of ab rows and one column obtained by Equation (20) into arows and b columns.

Fortran code nnls.f implemented on the basis of a document “Lawson, C.L. and R. J. Hanson, Solving Least Squares Problems, Prentice-Hall,1974, Chapter 23.” and nnls.c as a C-language version thereof are widelyknown for performance of the optimization process of Equation (20). Inaddition, Matlab (registered trademark), Python (registered trademark)and the like also provide a tool for easily performing the optimizationprocess of Equation (20).

Second, as an improvement in the factorial HMM, the parameter estimatingsection 22 fixes the number of states K to two (K=2) corresponding toonly an on state and an off state, and sets one of a first column and asecond column of the observation probability parameter W to a zerovector at all times. This assumes that the electric apparatuses 12assume only two states, that is, an on state and an off state, and thata power consumption in the off state is zero at all times.

The constraint condition of the two on and off states has advantages inthat a result of parameter estimation of the factorial HMM correspondsto actual modes of operation of the electric apparatuses 12 and that thestability of parameter learning of the factorial HMM is improved. Somesimple electric apparatuses 12 such as an incandescent lamp and the likecompletely satisfy the constraint condition of the two on and offstates. Even in a case of electric apparatuses 12 having a plurality ofmodes of operation, a result of parameter estimation of the factorialHMM better corresponds to actual modes of operation of the electricapparatuses 12. One of reasons that a result of parameter estimation ofthe factorial HMM does not correspond to actual modes of operation ofthe electric apparatuses when this constraint condition is not added isthe occurrence of a number of local solutions that do not correspond inactuality but have high likelihood because the factorial HMM is a methodhaving a very high power of expression.

M Step Process by Parameter Estimating Section 22

An M step process by the parameter estimating section 22 will bedescribed with reference to FIG. 15.

Incidentally, processes other than the M step process are similar to theprocesses described with reference to FIG. 10 and FIG. 11, FIG. 13 andFIG. 14, and the like. However, there are differences in thatobservation data Y stored in the time series retaining section 44 isconverted into only positive values, and in that the number K of statesof the parameter W^((m)) is two (K=2) and one of a first column and asecond column of the parameter W^((m)) is a zero vector at all times.

In step S81 of the M step process, the parameter estimating section 22obtains initial state probability π^((m)) for all the factors m=1 to Mby the above-described Equation (13).

In step S82, the parameter estimating section 22 obtains statetransition probability A_(i,j) ^((m)) from a state S_(j) ^((m)) to astate S_(i) ^((m)) for all the factors m by the above-described Equation(14).

In step S83, the parameter estimating section 22 obtains an observationprobability parameter W by the constrained quadratic programming ofEquation (20) without using the above-described Equation (15).

In step S84, the parameter estimating section 22 obtains a covariancematrix C by the above-described Equation (16).

The M step process is performed by the parameter estimating section 22through the above processes.

Process of Identical Apparatus Determining Section 24

A process of the identical apparatus determining section 24 will next bedescribed.

FIG. 16 shows results of parameter estimation when an electric apparatus12 whose original number K of states is three (K=3), for example anelectric fan having three modes of operation, that is, a stop mode, aweak operation mode, and a strong operation mode, is estimated under theconstraint condition of two on and off states.

When the electric apparatus 12 whose original number K of states isthree (K=3) is estimated under the constraint condition of two on andoff states, the results of parameter estimation converge to apossibility A or a possibility B in FIG. 16.

In FIG. 16, a first state and a second state as estimation results whenestimated as the two on and off states are described as “ON” and “OFF”to facilitate understanding. In practice, for example, supposing thatthe first state is OFF, and that the second state is ON, S_(t)^((m))=[First State, Second State]=[0.0, 1.0] at a time of ON, or S_(t)^((m))=[First State, Second State]=[0.9, 0.1] at a time of OFF.

The possibility A represents the strong operation as a simultaneousoccurrence of two factors. The possibility B represents the weakoperation and the strong operation by two factors separately.

The possibility A and the possibility B each model states of operationof the electric fan correctly, but do not show, as they are, whether thetwo factors represent two separate electric apparatuses 12 or differentmodes of one electric apparatus 12. However, it is known that thefollowing conditions hold at all times in the case of one electricapparatus 12.

Possibility A:

Necessary condition: When S⁽²⁾=ON, S⁽¹⁾=ON at all times.

Hint condition: When a transition is made from S⁽¹⁾=ON to S⁽¹⁾=OFF, atransition may be made from S⁽²⁾=ON to S⁽²⁾=OFF at the same time.

Hint condition: When a transition is made from S⁽¹⁾=OFF to S⁽¹⁾=ON, atransition may be made from S⁽²⁾=OFF to S⁽²⁾=ON at the same time.

Possibility B:

Necessary condition: When S⁽¹⁾=ON, S⁽²⁾=OFF at all times.

Necessary condition: When S⁽²⁾=ON, S⁽¹⁾=OFF at all times.

Hint condition: When a transition is made from S⁽¹⁾=ON to S⁽¹⁾=OFF, atransition may be made from S⁽²⁾=OFF to S⁽²⁾=ON at the same time.

Hint condition: When a transition is made from S⁽¹⁾=OFF to S⁽¹⁾=ON, atransition may be made from S⁽²⁾=ON to S⁽²⁾=OFF at the same time.

Thus, by evaluating the conditions, it is possible to estimate that thestate S⁽¹⁾ and the state S⁽²⁾ originate in an identical electricapparatus 12.

FIG. 17 is a flowchart of an identical apparatus determining process bythe identical apparatus determining section 24.

First, in step S101, the identical apparatus determining section 24generates all combinations of each factor of the factorial HMM asidentical apparatus candidates.

In step S102, the identical apparatus determining section 24 selectsidentical apparatus candidates according to necessary conditions.Specifically, the identical apparatus determining section 24 selectscombinations that satisfy a necessary condition at all times among allthe combinations of each factor as identical apparatus candidates.

In step S103, the identical apparatus determining section 24 selectsidentical apparatus candidates according to hint conditions.Specifically, the identical apparatus determining section 24 determinesthat a combination that has satisfied a hint condition Z times or more(Z is an integer of two or more) among the combinations selected in stepS102 is factors of an identical apparatus. The identical apparatusdetermining section 24 stores information on the factors determined tobe the factors of the identical apparatus in the database 23, and thenends the process.

General Process of Electric Apparatus Estimating Device 1

A general process of the electric apparatus estimating device 1 will bedescribed with reference to a flowchart of FIG. 18.

First, in step S121, the sensor section 21 measures a total value ofcurrent consumed within the house as observation data Y, subjects theobtained total value to preprocessing that converts the total value intoonly a positive value, and then stores the resulting value in the timeseries retaining section 44.

In step S122, the parameter estimating section 22 performs a parameterestimating process for estimating the model parameter of the factorialHMM on the basis of observation data {Y₁, Y₂, Y₃, . . . , Y_(t), . . . ,Y_(T)}. Then, the model parameter (value of the model parameter) as aresult of the estimation is stored in the database 23.

The parameter estimating process by the completely factorizedvariational method in FIG. 10 or the parameter estimating process by thestructured variational method in FIG. 13, for example, is performed asthe parameter estimating process in step S122. However, the M stepprocess in FIG. 15 is performed as the M step process of step S3 in FIG.10 or step S43 in FIG. 13.

In step S123, the identical apparatus determining section 24 performsthe identical apparatus determining process described with reference toFIG. 17.

In step S124, the state predicting section 25 performs a predictingprocess of predicting the state of the factor m in the future afterpassage of a predetermined time from a present time. Specifically, thestate predicting section 25 obtains the state probability S_(T+L)(m) ofthe factor m at time T+L, which state probability indicates the state ofthe factor m at a future time T+L after passage of a predetermined timeL from a present time T, by the following Equation (21).

$\begin{matrix}{S_{T + L}^{(m)} = {\prod\limits_{l = 1}^{L}{{P\left( {S_{T}^{(m)}❘S_{T - 1}^{(m)}} \right)} \cdot S_{T}^{(m)}}}} & (21)\end{matrix}$

The state predicting section 25 outputs a result of the prediction to arelated apparatus controlling section not shown in the figures, forexample, and then ends the process.

According to the electric apparatus estimating process of the electricapparatus estimating device 1 described above, observation data Y formedby a total value of current consumed by the plurality of electricapparatuses 12 installed in the respective places within the house ismodeled by the factorial HMM, and a model parameter is obtained. Thisfactorial HMM is an improvement on the factorial HMM in the past forapplication to NILM. Specifically, the electric apparatus estimatingdevice 1 converts the observation probability parameter W^((m)) into anonnegative value, and obtains the observation probability parameterW^((m)) by constrained quadratic programming. In addition, the electricapparatus estimating device 1 fixes the number of states K of statevariables S⁽¹⁾ to S^((M)) to two (K=2) corresponding to only an on stateand an off state, and sets one of a first column and a second column ofthe observation probability parameter W to a zero vector at all times.In other words, under constraint conditions that the number of statesassumable by each state of the factorial HMM be two, that theobservation probability parameter W^((m)) corresponding to the patternof a current waveform of the factor m of the factorial HMM benonnegative, and that one of the first column and the second columncorresponding to the number of states be zero at all times, a likelihoodfunction as a degree of the factorial HMM describing the pattern of atotal value of consumed current indicated by time series data ismaximized, whereby the observation probability parameter W^((m)) as amodel parameter is obtained. Thereby, a need for an advance database iseliminated, and the model parameter of the factorial HMM can be obtainedeasily. That is, a method can be established which facilitates thecalculation and adjustment of the parameter and which eliminates a needfor an advance database.

The series of processes described above can be carried out not only byhardware but also by software. When the series of processes is to becarried out by software, a program constituting the software isinstalled onto a computer. The computer includes a computer incorporatedin dedicated hardware or for example a general-purpose personal computerthat can perform various functions by installing various programsthereon.

FIG. 19 is a block diagram showing an example of hardware configurationof a computer performing the series of processes described above by aprogram.

In the computer, a CPU (Central Processing Unit) 101, a ROM (Read OnlyMemory) 102, and a RAM (Random Access Memory) 103 are interconnected bya bus 104.

The bus 104 is further connected with an input-output interface 105. Theinput-output interface 105 is connected with an input section 106, anoutput section 107, a storage section 108, a communicating section 109,and a drive 110.

The input section 106 is formed by a keyboard, a mouse, a microphone andthe like. The output section 107 is formed by a display, a speaker andthe like. The storage section 108 is formed by a hard disk, anonvolatile memory and the like. The communicating section 109 is formedby a network interface and the like. The drive 110 drives a removablerecording medium 111 such as a magnetic disk, an optical disk, amagneto-optical disk, a semiconductor memory or the like.

In the computer configured as described above, the CPU 101 for exampleloads a program stored in the storage section 108 into the RAM 103 viathe input-output interface 105 and the bus 104, and then executes theprogram. Thereby the series of processes described above is performed.

The program executed by the computer (CPU 101) is for example providedin a state of being recorded on the removable recording medium 111 as apackaged medium or the like. In addition, the program can be providedvia a wired or wireless transmission medium such as a local areanetwork, the Internet, digital satellite broadcasting or the like.

In the computer, the program can be installed into the storage section108 via the input-output interface 105 by loading the removablerecording medium 111 into the drive 110. In addition, the program can bereceived by the communicating section 109 via a wired or wirelesstransmission medium and installed into the storage section 108. Further,the program can be installed in the ROM 102 or the storage section 108in advance.

It is to be noted that in the present specification, the steps describedin the flowcharts may be not only performed in time series in thedescribed order but also performed in parallel or in necessary timingsuch as at a time of a call being made, for example, without beingnecessarily performed in time series.

In addition, in the present specification, a system refers to anapparatus as a whole formed by a plurality of devices.

It should be understood that various changes and modifications to thepresently preferred embodiments described herein will be apparent tothose skilled in the art. Such changes and modifications can be madewithout departing from the spirit and scope and without diminishing itsintended advantages. It is therefore intended that such changes andmodifications be covered by the appended claims.

The application is claimed as follows:
 1. A data processing apparatuscomprising: a data obtaining section configured to obtain time seriesdata on a total value of a plurality of waveforms or vector time seriesdata; a parameter estimating section configured to obtain a modelparameter when states of said plurality of waveforms or vector timeseries data are modeled by a stochastic model on a basis of saidobtained time series data or said vector time series data, wherein in aparameter estimating process based on an algorithm which alternatelycalculates expectation and maximizes likelihood, under constraintconditions that converted values or a vector of an observation parameterW(m) corresponding to a pattern of a waveform of a factor m of saidstochastic model are nonnegative, said stochastic model maximizes alikelihood function as a degree of said stochastic model describing apattern of the total value of said waveforms indicated by said timeseries data, whereby said parameter estimating section obtains theobservation parameter W(m) as said model parameter; a state predictingsection predicting a state of said factor at a future time after passageof a predetermined time from a present time; and an apparatus controlsection controlling a predetermined apparatus based on a resultpredicted by state predicting section.
 2. The data processing apparatusaccording to claim 1, wherein, in said parameter estimating processbased on said algorithm, under constraint conditions that a number ofstates assumable by each state of said stochastic model is two, saidstochastic model maximizes said likelihood function as a degree of saidstochastic model describing said pattern of the total value of saidwaveforms indicated by said time series data.
 3. The data processingapparatus according to claim 1, wherein, in said parameter estimatingprocess based on said algorithm, under constraint conditions that one ofa first column and a second column corresponding to said number ofstates is zero at all times, said stochastic model maximizes saidlikelihood function as a degree of said stochastic model describing saidpattern of the total value of said waveforms indicated by said timeseries data.
 4. The data processing apparatus according to claim 1,further comprising an identical determining section determining that aplurality of predetermined factors of said stochastic model correspondto the time series data.
 5. The data processing apparatus according toclaim 4, wherein said identical determining section selects combinationsthat satisfy a necessary condition at all times among all combinationsof each factor of said stochastic model as identical candidates, anddetermines that a combination that has satisfied a hint condition apredetermined number of times among the selected identical candidatescorresponds to the time series data, whereby said identical determiningsection determines that the plurality of predetermined factorscorrespond to the time series data.
 6. The data processing apparatusaccording to claim 1, further comprising an apparatus identifyingsection identifying a plurality of apparatuses corresponding to eachrespective waveform.
 7. The data processing apparatus according to claim6, further comprising a display unit displaying a state of saidplurality of apparatuses identified by apparatus identifying sectionbased on the result estimated by said parameter estimating section. 8.The data processing apparatus according to claim 1, wherein said dataobtaining section obtain time series data on a total value of waveformon predetermined cycles synchronizing phase of a measured value.